Hybrid Systems to Select Variables for Time Series Forecasting Using MLP and Search Algorithms
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
International Journal of Hybrid Intelligent Systems - Feature and algorithm selection with Hybrid Intelligent Techniques
Harmony search for generalized orienteering problem: best touring in China
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
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Lately, the research related to time series forecasting has been an area of considerable interest in different fields. It is very important to predict the behavior of the time series but it is not an easy task. Several models to aim this issue have been developed over the years, taking into account their peculiarities. Artificial Neural Networks (ANNs) are one of them. ANNs received much attention, and a great number of papers have reported successful experiments and practical tests. In this paper, a hybrid approach is proposed based on Harmony Search (HS) to select the number of hidden neurons and their weights for Extreme Learning Machine (ELM) algorithm, called HS-ELM. In addition, we provide experimental results from the application of our algorithm HS-ELM in real stream flow time series to show its effectiveness and usefulness.